Abstract
Human intrusion detection is widely used in intelligent video surveillance systems. It requires not only high accuracy but also real-time performance. In this paper, a real-time human intrusion detection algorithm is proposed to achieve good trade-off between detection accuracy and real-time performance: Firstly, fast HOG-based human recognition is designed, where HOG feature based human recognition is used to increase the detection accuracy, and one spatial-temporal joint detection region shrinking method is developed to reduce the computational load. Considering that the recognition accuracy of HOG-based human detection will drop markedly under occlusion, footstep recognition and a Bayesian Network based video-audio fusion model are proposed to achieve joint decision, which can improve the detection robustness further. Experimental results show that: compared with the existing methods, the proposed scheme can achieve better balance between the time consumption and detection accuracy.
This work was supported by the NSF of China under grant No.61001147,61171172, the China National Key Technology R&D Program under grants No. 2012BAH07B01, and by the STCSM of Shanghai under grant No.12DZ2272600.
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References
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Zhu, Q., Yeh, M.-C., Cheng, K.-T., Avidan, S.: Fast Human Detection Using a Cascade of Histograms of Oriented Gradients. In: CVPR, pp. 1491–1498 (2006)
Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: ICCV, pp. 32–39 (2009)
Cristani, M., Bicego, M., Murino, V.: Audio-Visual Event Recognition in Surveillance Video Sequences. IEEE Transactions on Multimedia 9(2), 257–267 (2007)
Dong, Z., Gatica-Perez, D., Bengio, S., McCowan, I.: Semi-supervised adapted HMMs for unusual event detection. In: CVPR, pp. 611–618 (2005)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: CVPR, vol. 2, pp. 246–252 (1999)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Srar. Soc. 39, 1–38 (1977)
Reynolds, D.A., Rose, R.C.: Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Transactions on Speech and Audio Processing 3(1), 72–83 (1995)
Yang, B., Busch, C., de Groot, K., Xu, H., Veldhuis, R.N.J.: Decision Level Fusion of Fingerprint Minutiae Based Pseudonymous Identifiers. In: Hand-Based Biometrics (ICHB), pp. 1–6 (2011)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, pp. 150–197. Morgan Kaufmann, San Mateo (1988)
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Wang, D., Zheng, S., Zhang, C. (2012). Real-Time Human Intrusion Detection Using Audio-Visual Fusion. In: Zhang, W., Yang, X., Xu, Z., An, P., Liu, Q., Lu, Y. (eds) Advances on Digital Television and Wireless Multimedia Communications. Communications in Computer and Information Science, vol 331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34595-1_12
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DOI: https://doi.org/10.1007/978-3-642-34595-1_12
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